Linear Logistic Models with Relaxed Assumptions in R
Linear logistic models with relaxed assumptions (LLRA) are a flexible tool for item-based measurement of change or multidimensional Rasch models. Their key features are to allow for multidimensional items and mutual dependencies of items as well as imposing no assumptions on the distribution of the latent trait in the population. Inference for such models becomes possible within a framework of conditional maximum likelihood estimation. In this paper we introduce and illustrate new functionality from the R package eRm for fitting, comparing and plotting of LLRA models for dichotomous and polytomous responses with any number of time points, treatment groups and categorical covariates.
- Fischer, G. (1993). Linear logistic models for change. In G. Fischer & I. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.Google Scholar
- Fischer, G., & Ponocny, I. (1993). Extending rating scale and partial credit model for assessing change. In G. Fischer & I. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.Google Scholar
- Hatzinger, R., & Rusch, T. (2009). IRT models with relaxed assumptions in eRm: A manual-like instruction. Psychological Science Quarterly, 51, 87–120.Google Scholar
- Mair, P., Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20, 1–20Google Scholar
- R Core Development Team. (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
- Sarkar, D. (2008). Lattice: Multivariate data visualization with R. New York: Springer.Google Scholar